class_docstr: Creates patches of cells.
  
  Parameters
  ----------
  base_dataset: CellsDataset
      Dataset of cells
  patch_size: tuple of ints (default=(256, 256))
      The size of each patch
  random_flips: bool (default=False)
      If true, patches and masks will be randomly flipped horizontally and
      vertically.
  padding: int (default=16)
      Amount of paddding around each image and mask
class_docstr_tok: ["Creates","patches","of","cells",".","Parameters","--","--","--","--","--","base_dataset",":","CellsDataset","Dataset","of","cells","patch_size",":","tuple","of","ints","(","default=","(","256",",","256",")",")","The","size","of","each","patch","random_flips",":","bool","(","default=False",")","If","true",",","patches","and","masks","will","be","randomly","flipped","horizontally","and","vertically",".","padding",":","int","(","default=16",")","Amount","of","paddding","around","each","image","and","mask"]
code: def __len__(self):
      return len(self.coords)
  
file_path: skorch/examples/nuclei_image_segmentation/dataset.py
imports: from itertools import zip_longest, product, chain
  import random
  import numpy
  import torch
  from torch.utils.data import Dataset
  from torchvision.transforms.functional import pad, to_tensor, normalize, hflip, vflip, crop
  from PIL import Image
label_desc: Use image node_id 2 for calling the PatchedDataset obj's underlying member method code with example usage: obj.__len__() and returns: len